A fraction of those Marketers / Directors will calculate Conversion Rates for those marketing campaigns. They deserve our love. [And if they measure Micro Conversions they deserve our love AND respect for exhibiting savviness by using economic value.]

But all of the above is still focusing on short term success. Even measuring Visitor conversion rates (Visit based conversion rates promote bad marketing behavior) is akin to declaring success after a one night stand.

I reserve the best hugs, kisses, smiles, love, respect and my deepest admiration for Marketers and Analysts who use Lifetime Value computations!

That is focusing on real success, not simply the first conversion (the one night stand!).

That is focusing finding the customers that create value for the company, long term.

That is truly doing the kind of Analysis Ninja work that solves tomorrow's problems today!

For the above reasons I have been meaning to write a post on computing Lifetime Value for a very long time. But perhaps a better idea is to get an expert to do it, the result will clearly be far better than anything I would write. So I emailed my friend David. : )

David Hughes is the Co-Founder of the email marketing consultancy called The Email Academy and the author of one of my most beloved phrases: Non-line Marketing! His blog, Non-line Blogging, is a favourite of mine.

There are a handful of people in the world I could spend the whole day talking work and still have things left over to discuss, to learn. David is one of those super-smart, funny, and nice people. I have consistently found his ideas to be practical, grounded in common sense and instantly useful.

I could not be more thrilled that he agreed to cover this tough, yet rewarding, topic.

In this post David covers:

Why Life Time Value is important (especially in context of Acquisition)

How to optimally leverage value based segmentation & Lifetime Value

Share a sample analysis and, this is so sweeeet, a spreadsheet with a sample model that you can use to jump start your own LTV journey!

Buckle up, this is going to be fun and it just might change your life! :)

Here's David. . .

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Solve tomorrow's problems today – introducing Life Time Value.

Acquiring new customers isn't getting any easier: We've picked off the low-hanging SEO fruit, we're paying more for quality clicks in AdWords and the going rate for affiliate deals just keeps getting higher.

We are also haunted by the specter of "marginal cost": The more customers you need, the more impressions and clicks you need. But as we drill deeper into worse performing media, or pay out for lower-volume-lower-relevance search terms, our cost per sale gradually rises.

There is a better way to analyze your acquisition strategy than simply using Conversion Rates or Cost Per Acquisition (CPA). Using Life Time Value might be a much better idea.

Life Time Value (LTV) will help us answer 3 fundamental questions:

1. Did you pay enough to acquire customers from each marketing channel?

2. Did you acquire the best kind of customers?

3. How much could you spend on keeping them sweet with email and social media?

I'm going to suggest that maybe you should be paying significantly more money for the right customers.

Let's start at the very beginning…

…that's a very good place to start. Take a snapshot of your customer database for the past 2 years and it may look like this:

That is an average.

The trouble with averages is they conceal all the really interesting stuff that's going on beneath the surface.

If you look beyond the averages you'll find that some of your clients are "better than average" and some are "worse than average".

Try and segment the customer base by total purchases over a longer time period, say a year, or total spend and you may come to a conclusion that says something like:

My most valuable customers last year bought 4 times compared to an average of 2. They tended to spend 40% more than average per order. However, they might cost significantly more to acquire.

Much better than the average right?

So, where did you get the valuable customers from?

Simply knowing that you are getting lots of conversions is not enough, you might just be getting new low value customers.

This is where Lifetime Value becomes interesting: Some companies are getting really worried about the lasting impact of "buying cheap customers".

For example, in many markets the price comparison intermediary (/engines) is an easy option – you pay your money (affiliate fees) and you take your customer.

But how likely are these customers to buy another product? Or hang around for a few years? With no brand affinity there's no desire to cross-buy and maybe we're filling up our databases with low value, promiscuous customers.

A simple segmentation by channel can easily help us answer these key questions. The output may tell the following story:

But, I hear you cry, Search Marketing is labour-intensive, risky and costly compared to buying customers at a fixed price from an intermediary.

OK, so let's look at how much MORE we should be paying for Mr Right, rather than Mr Average.

Let's change the headings of the table above to be clear what we're talking about…

Best and Average customers will have different Year 1 buying patterns:

Once we have done this basic segmentation we can then factor in the cost of acquisition per segment to determine the Net Profit per customer per segment.

You'll work with your acquisition team or your finance team to get the cost data. For some of your campaigns this data might not be easily available in your web analytics tool (it is also quite likely you are doing all of this analysis in Excel).

The table you'll end up with might look like this:

It should be pretty obvious at this point that simply taking the short-term view with metrics like Cost Per Acquisition (CPA) might not be prudent since you are rewarding the source sending you Mr. Right and Mr. Average just the same. Yet they are not of the same value to your business (Net Profit!).

It is important to move away from a cost-based acquisition model to one that recognises the cross and up-sell rewards of acquiring the right customers over the duration they'll be our customers.

Spend an extra $8.00 per customer, if you have to, and you're still twice as well off than buying rubbish ones!

But we can do so much more… let's take a longer term view.

Value-based Segmentation & Life Time Value.

By now we have established this: Some of your customers are going to be spending more with you, for longer.

Let's say I am a car insurance company, or a subscription publisher, with a desire to sort out some of tomorrow's problems today.

I know that the initial cost of acquiring customers (or policies/subscriptions) will only go up as more of my competitors sail for the calm waters of "cost per acquisition" pricing.

So, if I need to sell 10,000 policies every year I have 2 options.

Buy cheap customers and hope that a few may buy again

Buy the right customers that stay with me for 2 or even 3 years

Without doing the value-based segmentation we'll never understand which channels bring in the best customers and that would be a terrible shame.

The ground truth is that I can re-new a policy or subscription for considerably less than buying a new one. How?

One strategy might be to spend an extravagant $1.00 of marketing costs to show my love an appreciation to our customers throughout the year via email or social media, increasing the chances they'll buy again.

That means I won't have to spend $20.00 buying a new one… a saving of $19.00 per renewal.

So if I can grow my repeat purchase rate from 20% to 40% that means I will generate 2,000 policies at $1.00, not $20.00.

That's a $19.00 savings on each of the 2,000 policies. BAM!!

Moving to a Life Time Value acquisition strategy will save my company $38,000. Not bad for a couple of days work.

Let's finish off the concepts of value based segmentation and lifetime value by going back to the original example we were working through.

If we can identify channels, campaigns, media or propositions that deliver "better than average" customers we can begin to see how much more profitable they are and decide how much more we should be spending on them.

Here's the (sample) analysis I (or you!) would do:

Ladies and Gentlemen – select your lifetime!

In the above example I've modelled a 3 year lifetime – that would be sensible for a typical consumer e-commerce player.

Publishers and financial services companies may take a longer term view… certainly off-line we have been building 5 year plans in publishing for decades.

If you're more comfortable with 6 months or 18 months, then go for it!

If you do you'll need to be looking backwards and forwards at the same time.

You may only have 6 months of on-going data for some segments, but use that as a starting point and build some simple scenarios from there:

What if 50% of them spent 10% more in the next 12 months?

What if 30% of them spent 40% more in the next 6 months?

Over time as you replace modelled data with real data you should be able to re-weight your acquisition spend, replacing one affiliate with another as the cross and up-sell orders begin to roll in (i.e. the customers you acquired begin to make additional purchases from you).

By rewarding the better partners / media / acquisition channels with higher CPA's you'll be building a defensive position that prevents competitors buying their way into the good sources ("How can they afford to pay THAT MUCH?!" they'll all be wondering). It will be our little secret.

Life Time Value is for Life, not just for Christmas.

We've really only just scratched the surface of LTV in this blog post.

Many people have devoted their whole careers to unlocking its mysteries so apologies to all of them for the "top level" content here.

However, it is a concept that deserves the attention of a new generation of digital marketer and it will alter the way many companies approach acquiring and retaining customers.

__________________________________________________

Amazed?

Don't cha feel a little bad that you were making decisions about where to invest your precious marketing dollars based on either Conversion Rate or based on Cost Per Acquisition?

What's scary is that you could currently be using Conversion / Average Order Size / Cost Per Acquisition to invest more in one particular channel, all the while, unbeknownst to you, shoveling "poor quality" customers. Or "high CPA's" might have caused you to not spend enough on a channel where you can get lots and lots of high value customers.

Scary! Yet exciting that finally you can be so much smarter!!

Bonus: As a very special treat David's created an Excel spreadsheet to help jumpstart your Lifetime Value journey.

The spreadsheet has two tabs.

Comparison LTV lets you model two segments of customers by helping you walk through clearly articulated questions.

Detailed LTV kicks things up a few notches by allowing you to make better decisions by modeling out the long term performance for a given customer segment. [Create more copies of this tab to model out multiple customer segments and then compare / contrast to make wiser decisions.]

[Please do not click on the link above, rather right mouse click and choose Save Link As or equivalent in your browser. Thanks.]

Closing Operational Thoughts:

I wanted to add a few thoughts about the operational things you need to worry about / keep in mind, as you revolutionize your company by using LTV:

1. You'll notice instantly that almost none of the data above is available in your web analytics tool. Not Omniture's Site Catalyst, WebTrends Analytics, Coremetrics, Google Analytics or Unica or whatever. This type of PII and financial data does not exit in these tools (often for a very very good reason).

Even the web analytics tools that say they create Lifetime Individual Visitor Experience (LIVE) profiles to compute Customer Lifetime Value (CLV) won't have the key Margin or multi-channel data, and hence not truly allow you to do the above, contrary to what might have been implied.

Web Analytics tools, even ones with lifetime visitor profiles, usually can't even stich together one person's clickstream behavior over the long term because of cookies and other data erosion issues. So plan on looking outside.

2. [Because of reasons immediately above and more…] Remember to focus not on the "Individual Customer", rather focus on the acquisition channel by analyzing segments of customers.

Individual anything ("you can track every single customer and understand every single customer and react to them in real time!!!") is over-rated.

Your BFF will be the Finance team, both to initially teach you some of the financial intricacies and give you access to data you need. Look 'em up. Take 'em out for dinner. Trust me when I say that the LTV work will be a tremendous asset to your career and expose you to the highest levels of your organization. A really really good thing.

4. You are going to have to darn near sleep with your IT team/person to ensure the key meta-data required to do this analysis passes from your website to the sources mentioned in #3 above.

For example in my first job I had to request (ok beg) the corporate IT team (ok one person) to enhance the corporate system with two columns so each web order order could be distinctly identified and contain "campaign id" and "acquisition cost".

The lack of this meta-data is where most LTV efforts fail.

Even if you are a 100% web business you'll have to ensure the "backend system" that contains this key web analytics meta-data else you are doomed. Sorry.

5. If you are multi-channel company (web, call center, stores, catalogs) you'll want to ensure an equivalency exists in your backend system to [A] track the same customer's multi-channel orders correctly [B] contain cost data from all multi-channel campaigns.

This is really really hard to do. Don't try to climb mount Everest on day one. Start small and build over time. Remember David's tips on making do with just what you have.

I want you to be aware of these few valuable lessons I have learned in my own journey. I had to learn them the hard way. : )

If LTV sounds like it needs effort and love then you have understood it correctly. Everything worth it is hard in life, but if you put in the effort you'll create an enviable advantage for yourself and your company.

In closing:

1. Focus on long term success, acquiring truly valuable customers…

2. by embracing Net Profit and Lifetime Value…

3. and becoming BFF's with the Finance Team, good things will come of it!

Good luck!

Ok now your turn.

Have you used lifetime value or other such metrics to enhance your acquisition strategies? What was your experience like? If you have not used LTV, do you plan to use it now? What did you find to be of most value in this post? What would you disagree with? Did you want to run to England and give David a hug? :)

Comments

Once you get the initial tracking in your backend systems sorted out, it is truly amazing what details you can dig out. And it is really not that hard to do!

Just have an extra field on each customer and each order indicating where both the customer and order originated from. And I bet you can pull those details (medium/source) out of the Google Analytics tracking code and store along side both the customer and the order. Really, it is pretty simple to get up and running.

I have one problem with the LTV model though: If looking at it blindly it will always favor your oldest running campaigns.

Say I ran a special e-mail campaign 4 months ago. When looking at the LTV of ALL my marketing campaigns, the regular ones will always out-perform the e-mail campaign. Simply because the new customers/orders that the campaign generated hasn't accumulated enough LTV yet.

Marketing campaigns are usually evaluated once every month or every 6 months. How do you avoid that your not-so-analytics-savvy-marketing-boss will come to the conclusion, that some of your new initiatives are performing like rubbish, just because his favorite business intelligence tool reports a low LTV for those campaigns?

LTV tracking is indeed an extremely powerfull tool, especially in out-bidding your competitors. But make sure your staff (esp. management) is educated well about the pitfalls.

Soren, the way you get away from the issue you are having is by adding projected value of these segmets moving forward. For this you need to be able to measure historical churn rates per segment. 1/churn = expected length of time a customer will remain a customer. Then time x average spend = ttl expected future revenue.

Well said, my friend. If your web analytics efforts are not getting the kind of traction with senior management you would like to see, this is the way to get their attention!

I know it might be more *interesting* to chase down the latest front-end window into the process (iPad!) but in terms of value delivered, this kind of analysis is core. And, once you have this capability, you can value any kind of front-end data – devices, campaigns, phrases – within the LTV context.

Finally, don't let the word "LifeTime" bog down the conversation. Call it / determine "3 month value" or "6 month value" and you will be well on your way to delivering very powerful insight.

Excellent article, Avinash! All SaaS subscription-based companies need to focus on acquiring customers for the long run. As a sales-rep for a SaaS company, LTV is a critical metric and constantly talked about. Data is analyzed on an ongoing basis to identify the "best" LTV type customer.

New Customer Acquisition and compensation should be tied to an LTV metric in order to align the best interest of the company and the sales team. :)

Avinash/David,
Great post. LTV is definitely a metric worth losing sleep over – in order to learn, understand and setting it up.

What I liked about this post is that you articulated the concept very well — and that to me is the key.

Many a times, a HIPPO (or an analyst) wants to calculate the LTV and make the mistake of starting with the calculation/statistics/financial aspect before understanding the concept. The net result is that they are overhelmed with terms like NPV (net present value), interest rate, time-value of money etc. and the LTV effort never takes off.

As Jim said above, it does not matter what you call it – "3 month value" or "6 month value" or something else — the goal of the analyst ninja should be to really understand the fundamental reason why we are doing LTV in the first place and what it represents and set it up in as simple a fashion as possible (KISS principle is very appropriate here).

Wow this was a great post! Thanks for introducing us to David, Avinash. I love finding new blogs to follow.

Given Avinash's build-up, how rare is it to be thinking about lifetime value within the Web Analytics industry right now? Cutting edge? Something the top companies can demand out of new hires? Something most folks haven't hear about?

I love the timing of this blog post! This article goes really well with the webinar Google released 2 weeks ago about custom variables. Google evangelist indeed!

I also appreciate the faith you have in us readers and letting us know that our time is value. In a lot of your posts (especially about segmentation), I always get the feeling that you're mentoring us by showing us how to figure out opportunity cost that lies within any Analytics decision we make.

Avinash and David have teamed up to give us the simple yet profound concept of LTV. I can see the immediate usefulness and I am going to apply this concept to the huge data of customers. Will try to report the results here.

Thanks so much for this article. LTV has been on our agenda and is a hot topic which very few people know about or understand. I really liked how you broke it down to simple terms to show how to start thinking about it and figuring out who/what is involved in the process. I did want to go to England to give David a hug until I saw him hating on Thierry Henry in one of his blog posts and that made me change my mind. :)

Amazing post, there are some great ways to help foster retention through new web apps and creative advertising tools. We use some great tools like FlowTown and ReTargeter to make sure we are consistently in front of customers and encourage them to come back to our site.

However the problem is arriving at the life time value of a customer. With companies, especially the large ones, looking for specific numbers to back up the value computations, it is often misconstrued or ignored by the top brass. Also, the bean counters and most executives are more concerned about the short term (as you allude to the value of averages in the beginning of your post) rather than long term.

My hunch is that LTV can be much more meaningful, accurate and usable by the small and medium sized businesses to their advantages.

Great post! While it is great to look at RPV and Conversion, in my opinion this is just instant gratification. I think we need to start thinking long-term strategy and thus, the LTV concept is very timely. It is time to step out of the normal mode of thinking and look beyond the already overrated CVR!

I am going to explore the model that is used in this post & see how far I go!

You are right in that you need for a "lifetime" to pass before your pronounce judgment. One way to think about the issue you raised is that for brand new campaigns (say you have never tried Facebook Display campaigns) it does take a little while to truly judge what the "lifetime" value is, but you can break it down into smaller chunks as David (and Jim and Ned and Joe) have suggested. 3 months, 6 months etc and continuously refine.

For most of your campaigns though, say common strategies like email and affiliate and search etc, you'll have a very solid foundational base of data which you can use to make very very informed decision on the value of current campaigns and quality of customers they are acquiring. Then, as above, you keep refining as you have more data.

Thank you very much for bringing up this very key concept can concern.

Jim: Thank you! Your work (and seminal article: http://www.jimnovo.com/LTV.htm ) have been so instrumental in helping shape my own thinking on this topic. I am thrilled you found this post to be of value.

Ned: Great advice that we should try not to make the mistake of making things far more complicated than they need to be. So many LTV efforts end up in the graveyard due to that mental model. The sad thing is you can extract so much mileage from the simple strategies. Then when it is time to complicate things… go crazy! :)

Josh: It is exceedingly rare to find this type of work / mental model in the web analytics world. IMHO. Primarily because we are so stuck in reporting all kinds of hits and tagging the site and redoing the tags and buying multiple layers of tools to report on visits to the site. We rarely get the chance to take three steps back and focus on the strategic.

I hope David's post forces all of us to take a hard look at what we as Web Analysts are actually doing to add value to our companies.

Alex: Loved the comment about David's Online Reputation Monitoring post, it was a very good one. And, ok you are not going to give me a hug any more, I thought Mr. Henry deserved some of David's ribbing!! :)

Mark: Conversion rate is still of some value, but I have always advocated against an obsession with it (even without Lifetime Value computations).

Paddu: You are right that with the HiPPO's we have to articulate the value proposition of the metric clearly and show that it can help make better decisions. I would reflect the thought Ned mentioned in his comment: try not to overdo it. I think that is sound advice (even if we all, self included, have a very hard time following!).

I have to admit that in my experience the larger the company the more value LTV can add not just because of the things mentioned in this post, but it can also be a great neutral arbitrator of what is right and hence reduce the politics and other machinations that are so popular in large companies.

Oh what a beautiful post! Back in the day when I ran marketing for a travel company and all of our advertising was paper-based, lifetime value was the key metric for assessing the worth of any media. The "Sunday Times" was several times more expensive as a customer acquisition route, than any other media – but we gladly coughed up the cash, because we knew how valuable those customers were.

But now, working for a company where almost all customer acquisition is web based, it is so much more complex figuring out just which customers have come from where, what that channel cost, and where best value is coming from. We must fight to get there though.

My only reservation with LTV is in line with what Soren is saying – it is not a great way of evaluating new media/channels and can easily be used as a way of justifying "sticking with what you know"

Now, I have a totally random and slightly off topic question that I wondered if you have an answer to, Avinash. I am looking for a good resource (book, blog) on SEO for blogs – specifically the key differences between SEO-ing a blog and a standard website – any ideas you have would be much appreciated!

An interesting and insightful article as always. Whilst I bought in to the whole LTV concept a while back and this post has given me further insight, I'm not convinced the model outlined above goes far enough to optimise the channel acquisition mix (though I might well have missed something in my early morning fug!).

For me, what's missing is attribution. The model outlined above seems to relate to last click attribution but not drill down into stacking, the attribution of revenue to each campaign that contributes to the final sale.

When looking at what marketing channels to increase your spend in, surely you need to understand which ones are contributing to the final sales. If I take a last click approach, I might divert all my money to email as a retention tool for 'best customers' because that is the channel the majority use on their last click as it is an effective prompt.

However, using a stacking analysis I might discover that my social media activity is driving visits and maintaining customer engagement but the actual sale is then prompted by the email campaigns. In this case, I would attribute a % of the sale value to the social media activity as well. If I removed investment in social media, my email conversion is likely to suffer as well as my engagement levels drop.

In my mind you therefore need to be able to integrate analytics data into your financial modelling to build a more complete picture.

Great guest blog! Thank you David and Avinash for bridging the gap between a more traditional direct marketing concept and the digital marketing universe. What's great about his post is that it just makes sense. Why wouldn't we analyze which sources of traffic are delivering the most valuable customers? And why wouldn't we be willing to pay more for those uber-customers?

I worked on LTV analysis earlier in my career (pre-web analytics) and it just makes sense to bring that ability to identify, then target, your most valuable customers, on the web.

I am a subscriber to both your blogs, and this post justifies the joint presentation of well-know direct marketing analyses with web analytics.

I am greatly in favour of combining a longer-term horizon into web analytics and move away from over-reliance on conversion rates and ROI of first-time responders. This underestimates the complexity of situation, and can leave you under-estimating or overestimating the lifetime values of customers.

What I would recommend web analysts do is to get familiar with the process of calculating CLTVs from the customer database / ERPs *before* they try and marry up the clickstream data with the customer data. It's simply you need to get comfortable with the calculation before layering another dimension.

Can I suggest (if you haven't already thought about it) bringing in Kevin Hillstrom for a guest spot. He has lots of interesting ways of looking at online metrics and forecasting.

In implementing this, I have a similar concern as James in that folks are immediately going to balk at the concept because it doesnt take into account attribution.

I would say about 30% of our customers come in via a second (or third) source in the same visit. Would you ignore those somewhat fringe cases in favor of using a majority to dictate LTV? It seems that this might be the recommendation given that using individuals is not the point, but rather evaluating the channel.

Or perhaps, look for trends in the visitors who do come in via multiple channels (and hence increasing your costs) and factor that into the overall viability of that channel?

I understand the caution and we should tread carefully. But there is a way to deal with this, i.e. experiment and not kill things before you have had time to observe if they were "long term successful" (even if it is waiting eight or twelve or eighteen weeks to get the first blush of "LTV" data).

James (/Megan): You are not barking up the wrong tree but I humbly believe that you are merging two completely different issues that are individually hard to solve into one definitely unsolvable problem.

This comment space is not enough to do justice to a thoughtful reply so let me try to share a couple highlights and recommend that if you have my book Web Analytics 2.0 please jump to Page 358 and read the sections on Multi-touch Campaign Attribution Analysis.

Couple quick highlights: Trying to figure out how to "assign credit" for a conversion is a fatally flawed. Attribution is not the problem. Identifying the best media mix portfolio for our acquisition strategy is. In that context the methodology of LTV can still be applied, if only in the context of the customers you have acquired as a result of the controlled experiments you'll run to find the best mix.

I would recommend, as many have done in comments, taking one baby step at a time. Master media mix optimization for lowest CPA. Master application of LTV to optimize individual / group of channels. Work on trying to see who those two different things work together.

You've raised an important point and I appreciate that very much. Thank you.

Dan: A most excellent point on the order of doing things. 1. Get comfortable with the computations with even non-web data. 2. Go crazy on the web and move beyond CPA's and Conversions! Good point.

I thought I would add a couple of comments to Avinash's around the whole idea of LTV and attribution modelling. In the blog we wanted to illustrate a simple model that can be adapted to your own organistion's complexity, and we'd still be writing the blog that attempted to tackle last click referrals and LTV to EVERY market and channel!

Just to illustrate the futility that Avinash alluded to in the quest for first click/last click/balanced/weighted attribution models, lets not rule out the role of off-line factors too…here is a couple of lines from a blog on multi-channel attribution modelling:

The big problem with our lovely "closed" view of our customers' mind is that it is frequently polluted by mucky, grubby off-line advertising. Maybe it was a print ad that stimulated the click on a banner, or perhaps a direct mail pack thumping onto somebody's door mat that prompted a branded search.

So we see that backward looking attribution modelling is a job for life but a waste of time, but understanding what may have influenced a sale and testing that moving forward seems entirely reasonable.

Finally, how about looking where the lead originally came from, rather than obsessing about the touch-points through the process? For an insurance company, a quote from a comparison engine is easy to track through to sale and LTV and could have a profound impact on how you use that acquisition channel, regardless of whether they finally convert through Facebook or email.

Deven: Please see the closing operational thoughts section of the post, specifically points number 3, 4, and 5.

In a nutshell Google Analytics (or Omniture or WebTrends or CoreMetrics for that matter) are not doing to 1. store the data you need to do Customer LifeTime Value calculations or 2. keep the history (past/future) to allow you to compute multi-channel CLV. You'll do this outside your web analytics systems (more in the Operational Thoughts section).

I'm not sure that it's always necessary to do your own LTV modelling. If you've got an analytics provider that has enough focus on your industry (e.g. Kontagent for social gaming), you might still be able to get some good LTV-based information from them.

Math Ninja: I am skeptical that this data will be available in web analytics tools, not because the tools can't handle it but rather because of where the key pieces of data sit today (outside the analytics tools).

You are right that Kontagent is doing some cool stuff.

Dennis: CampaignID will help us connect to the meta-data we need for campaign analysis.

The reason for storing AcquisitionCost is that most of the time it will variable and not a constant.

Think of how your bids for search campaigns, even for the same keyword, will change. Or bounties for affiliate campaigns or CPM's or CPA's for display campaigns.

They change a lot so it is helpful to have the costs listed as well (easier analysis).

Maybe this is tangential to the main LTV discussion, but I am wondering then about the value of tracking and segmenting repeat customers in your web analytics tool. Or maybe I should look for other for insights, but don't bother to try to approximate LTV from your web analytics?

LTV is all about stepping back a bit from your analytics reports and taking in the view. Although it is unlikely that sales will come directly from a banner ad, or an affiliate link (branded search will still get most of the "last click" glory), analysis of where sales originally came from is a really interesting bit of work.

If you're lucky you may conclude that:
– affiliate network A or partner B tend to generate much better value customers than anybody else…let's give them more money as a thank you.
– people coming from web site A adverts or email list B addresses are better LTV customers than other sources so let's spend more with them.

You will need to consolidate data from maybe 2 or 3 sources to get to this point, and the report won't need to include all the granular detail that your analytics tool spits out. Just get a feel for where your best and least valuable customers come from…an excel spreadsheet with "source of first visit" and "are they Hi Med or Low value after 3, 6 or 12 months". If there is no pattern, try looking deeper at creative clicked, or offer.

Simple, but it could be the best day's analysis you'll ever do in order to align acquisition activity to long term profitability.

Michael: Web Analytics tools can help you focus on more longer term success oriented metrics.

For example my favorites Visitor Loyalty and Visitor Recency are both geared towards looking beyond a single visit mindset. Or simply using Unique Visitors in the denominator when computing Conversion Rates rather than using Visits.

So there is certainly progress to be made in moving in the right direction with Google Analytics or Yahoo! Analytics or Site Catalyst.

If you are able to segment Repeat Customers in your Web Analytics tools then you can and should do that analysis.

Sadly what analytics tools today provide is Repeat Visits and, some will provide, Repeat Visitors. Neither one of those two actually is Repeat Customers (someone who submitted a lead, multiple leads, or placed an order/multiple orders).

But if your tool provides Repeat Customers then use it because at least for the Web you'll have a "lifetime" view, that is a fantastic first step towards a multi-channel lifetime value.

To the comment #1, where there is a reference to recent campaigns not accumulated enough LTV yet, so LTV analysis favors longer-running campaigns. That is not the way to think about LTV, it is not past value, but future value.

LTV is forward looking not backward looking. Of course, you can and should look backwards to get an idea of the future, and you do need past data to estimate it, but you need to be looking forward the same distance in time for all your customers. Once you convince your managers that LTV is about future value, not past value, the time window issue will go away.

The longer this forward-looking horizon, the better your results will be. However, in real life you have to balance what you wish you knew with what you actually know about your customers. So I would recommend choose the shortest history (i.e. most unknown) customers that are of interest to you in an analysis, see how far in future you are comfortable predicting their behavior, and then bring in all your other customers to the same time horizon.

I think the link to the LTV spreadsheet in the article is broken. When I click on it, my browser (Firefox) opens a page with junk characters. I tried saving the file and that doesn't work either. Could you please fix the link?

Calculating the total lifetime value of a customer is just good business practice. I extends way beyond online advertising and into expenses for customer service etc. Any business that does not do it is mad.

I've been researching for this issue on the past week and found it really important.

What i'm trying to figure out now is, considering a freemium model for a web product, how to track the entire customer lifecycle, since his first visit to the time when he starts to pay to have premium account.

I need to tie the revenue generation to the first source that brought the user in order to derive the best one.

I'm struggling with Google Analytics and finding ways to track the cycle: Acquisition, Activation, Retention, Referral and Revenue. But the hardest thing is to tie the last to the first.

What do you think is the best way of getting this? Maybe move to a database level approach?

Lucca: Most of the data that you will need will be outside the web analytics tool, much of it in your "backend" databases (customer relationship management and erp systems – no matter how simple or how sophisticated they are).

The web analytics tool you use, any one, will serve as one important source of data, but just one. So you are better off with that optimal approach.

You'll notice instantly that almost none of the data above is available in your web analytics tool. Not Omniture's Site Catalyst, WebTrends Analytics, Coremetrics, Google Analytics or Unica or whatever. This type of PII and financial data does not exit in these tools (often for a very very good reason).

I have been working with "period" time value for a while and I am stuck with one problem:

How do you impact the cost of advertising of one channel when the channel's performance impacts the lifetime value of another channel?

To give you an example, let's say I advertise on both Affiliates and SEM but I know that some of my members aquired through my Affiliates campaigns are now spending thanks to my SEM campaigns.

In other word, the LTV of members acquired through Affiliates depends on my budget on SEM!

I am thinking, to cancel these cross channel performance bias, of attributing to each sale, a cost that equals to their gros margin in order to, in one hand not increase the Lifetime value of my affiliates Channel, and in the other hand, decrease the cost of acquisition of the members of my SEM channel.

Like that, all sales generated through SEM with members from Affiliates will be netural for the LTV of my affiliate channel and I will have a much clearer understanding of the real performance of my SEM channel.

I am not sure this is the right way to do? Anyone would have encountered this type of problem?

Part of this is a methodology challenge and part is a data / duration problem.

Optimizing for multiple channels, rather than silos, is now fairly standard. Methodologies like Media Mix Modeling powered by sophisticated controlled experiments is one way to go. You use math, design of experiments, test and optimize for a near term metric, say like CPA and find the optimal media mix.

When it comes to leveraging longer term metrics, like CLV, it is simply a matter of keeping the original set of acquisition channels (above) in the record and going back and looking at which mix got you customers that stayed with you the longest/bought repeatedly/resulted in most revenue. To do this you have to have the type of data stores where this data can be kept and analyzed.

The combination above is often available at larger companies that over invest in analysts.

Greg, sometimes we have to step back and make sure we understand the purpose of the analysis and what can be done with it once we have an "answer". Avinash's answer above is awesome and a good example – what is it the analysis is designed to answer and what action will be taken, do you care most about cost per new customer or LTV in this case?

1st case: Affiliate is known to often be a cannibalistic channel; are you sure the members "acquired through affiliate" did not start out in SEM in the first place, and ended up using an affiliate offer to become a member? What value is affiliate truly adding? Short of a full media mix model, can you turn off the affiliate program for a couple of weeks, compare results with affiliate on, and make an educated guess as to the real value affiliate adds?

2nd case: LTV is a different matter altogether. Example: often a low CPA leads to low LTV and high CPA leads to high LTV, so killing campaigns with high CPA acts to reduce profits over time. Further, you can't control campaign "sequence" and interaction; even if you knew the best Affiliate / SEM sequencing you could not really do anything to control this, so what is value of knowing?

First action is often predictive of future value (not first exposure, actually taking action) so which first actions create the most value over time? If indeed Affiliate is first action and SEM acts to create follow up value, then so be it, this is the way your business works. The real question is this: does first action = Affiliate create higher value than first action = SEM at 3 months? At 6 months? When does value created break even against acquisition cost, what is residual value stream after breakeven?

These are baseline questions you can actually answer and take action on. Trying to figure out the incremental value contribution of every media contact in every Lifecycle sequence is not only nearly impossible, it's not controllable by you in any significant way.

Often, the best answer to a business question is the most **reliable** one, even though the most reliable answer is not always the most accurate answer. Said another way, if indeed first action predicts 6 month value in a very reliable way, then do you really need "attribution" to specific media contacts during those 6 months? If the idea is to optimize acquisition for 6 month value, what additional value is created by understanding all the interactions in between (even if you could)?

This falls right in-line with a presentation I gave referring to a pyramid of marketing intelligence maturity where I discussed how the effectiveness of our decisions is ultimately inhibited by the quality of the data we base those decisions on.

By constantly striving to improve the local optima by which you base your decisions and putting in place the necessary data acquisition and curation to do so you company creates a competitive advantage that is difficult to match.

In your example you were analyzing an existing customer base. This is great for existing companies, but it's a problem for startups.

Figuring out LTV for a startup is a critical step for raising big funds, and can be a good metric for establishing a user base goal that will determine when outside funds should ideally be pursued.

What if you're trying to figure out the lifetime value of your product before you have paying customers? I'm thinking of looking at competitors. Look at how much money they raised versus how many users they had at the time. Do you think this is a good rough metric for LTV or do you have any additional suggestions?

They can either give it all to you wrapped in a nice bow (it does happen!).

You can make very rough calculations, though if we absorb the complexity of computing LTV in this post then it would be very hard.

We can get LTV numbers (usually some kind of averages) that are published by industry associations, industry analysts, or other such sources. They typically source this based on self-reported survey answers, but that is ok as it is the best we can hope for.

I have one question or you. I have a site where we conduct various online contests. Now success criteria for me is that participant comes and upload an entry in the contest. I want to start an affiliate program so I want my affiliates to get me more participants to my site.

How do I decide how much should I pay them for every sign up they get me? How will I decide what an average participant is worth for me?

Vinit: You have to calculate the customer lifetime value of people who sign up, and then segment them by your affiliates. :)

This does take some effort, but at the very minimum use the reports in your Multi-Channel Funnels reports to segment by your Affiliates what the near-term value is for the people you are signing up. The MCF reports will give credit to the Affiliate for both the last-click and any-click for your macro and micro-outcomes (if you have them all).

That could be a nice first step. After that you can get to LTV as you have more experience/data.

Hi Avinash, thanks for writing this post. It inspired me to write my own post about how to calculate the ROI of a small business website and has become a tool I use when I get new website inquiries.

When I read it for the first time back in 2010, I have to admit that ROI wasn't something I had to account for regularly; I was used to building websites and letting my customers give me feedback on whether or not it was improving there business and it hadn't crossed my mind about how I could actually calculate a potential ROI based on actual sales data.

It took me a while to wrap my head around the LTV, but from there I figured out that if I dig deeper, I could differentiate different customer types and extract and approximate what a typical customer was worth, I could provide a better potential ROI calculation based on the different customer types and sales from each group.

There's still lots of things for me to learn about LTV and ROI, but I just wanted to say thanks for sharing your analytics tip.

This is a really great post, but I don't see you talk about tools like KISSmetrics and Mixpanel on this blog. As it happens, both of these tools can track revenue along with tracking web analytics data (although it takes time to set up so it works accurately). They can also send marketing emails and keep track of individual customers.

It's like a web analytics tool, a revenue tracking tool, a CRM and a marketing automation/email marketing tool all rolled into one!

Surely, tracking metrics like Lifetime Value is MUCH easier with these tools? I would really appreciate your opinion on this! :)

Thanks for your comment Avinash! Implementing Mixpanel is on my to-do list and I plan to roll it out step-by-step in the coming month.

I did check out the Features page of Mixpanel and it can do the following: track individual people, their biodata and demographics, their purchase information and track all their actions on the web site and/or web app AND mobile as well. It can create segments based on user behavior, send behavior-targeted emails and lets you set up different automated email messages for different segments. It can track pageviews and all actions/goals on your website/app via its event-tracking model, and it's retroactive funnel tracking seems to be absolutely great. It can track acquisition data as well, and lets you compare the performance of different marketing channels. While comparing different attribution models is not possible, you CAN set it up to track with either first touch or last touch attribution.

To me, the above functions seem to encompass the functions of a CRM, marketing automation tool and a web analytics tool, bringing all the data into a single place. Based on my knowledge, I don't see what Universal Analytics can do more than Mixpanel can. I may be wrong about this because I'm simply romantic about the idea of a SINGLE tool for EVERYTHING you need to do! :) What are your thoughts regarding this?

Thanks Kaushik for this insightful post. Well I have a query on the customer lifetime value. Ours is an online real estate portal where in 95% of the scenarios repeat buy wont be possible atleast in next 5-6 years.

So in this scenario we consider his first & probably the last transaction to calculate the CLV?

DM: In your case, in your online systems it would be difficult to track multiple purchases – purely due to the span of time. (Though, if you implement Universal Analytics, and leverage User-ID-Override, you can still track all this behavior.)

You can track LTV in your offline systems where this information lives longer and more cohesively. Then, you can look at the behavior of your purchasers and use the LTV from there to set values for various Goals etc.

My question is once I have determined the LTV by channel for my best customers, am I suppose to take the 1st years LTV and use 33% of that as my CPA for these channels? Do I use the 2nd, 3rd years LTV? Is 33% of LTV for a CPA goal correct?

Mike: The LTV is the sum of the expected economic benefit that you will get from a customer. The total.

Sometimes LTV is evenly spread out (every year it is the same), sometimes it is front-loaded, and at other times it is back-loaded. It is important to know this from your personal business reality.

Then, what to contrast the CPA with. Or put another way, if the LTV is 100, is a CPA of 90 ok (because on paper you are still making money)? It depends.

My recommendation is to do the NPV (net present value) of the LTV if you count the entire sum of the LTV (because $10 two years from now is the the same as $10 one year from now). Then, go for a CPA as high as would still yield profit for your company.

I'm sorry if this seems a bit complicated, it is a complicated topic. I am so glad you are curious about it though.

This falls right in-line with a presentation I gave referring to a pyramid of marketing intelligence maturity where I discussed how the effectiveness of our decisions is ultimately inhibited by the quality of the data we base those decisions on.

By constantly striving to improve the local optima by which you base your decisions and putting in place the necessary data acquisition and curation to do so you company creates a competitive advantage that is difficult to match.

[…]
Introducing Customer Lifetime Value – David Hughes on Occam's Razor
Commentary: It was good to see this new post by my fellow IDM tutor David Hughes on the popular Occam’s Razor site since we rarely see LTV discussed online. A spreadsheet is available for download to get you started.
Implication LTV is a very well-established concept within direct marketing which everyone involved in digital marketing should be aware of and apply to help review the appropriate mix for customer acquisition and retention.
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Segments for Calculating Lifetime Customer Value
In this article from analytics experts, Avinash Kaushik and David Hughes, visit based conversion rates (or conversion rates based on single visits to a website) are compared lovingly to one-night stands. Lifetime value calculations and measurements provide deeper insight into the marketing lifecycle and how a series of clicks results in a customer, not just a single visit. Further, value based calculations that seek to find the right kinds of customers and where they come from can help save marketing dollars while simultaneously increase profits.
What this means to agencies: Much of the math in articles like this can seem tricky and intimidating, especially to right-brained clients who would consider themselves creatives before they call themselves “numbers guys”. Agencies can position themselves to take on this type of deep-dive analytics, customer database and segmentation work to bring about a sweeping change for clients.
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Avinash Kaushik, one of the leading experts on web analytics, just made an excellent point about not overlooking the lifetime value of a customer on his blog, Occam’s Razor. In it, he alleges, it is important to not merely view a customer by Cost-Per-Acquisition (CPA) or the cost to get a customer. Instead, it’s best to calculate the Lifetime Value (LTV or the total value that customer brings through spending) of each group of customers based on how you find them. Then do a simple, cost-benefit analysis. Where are you finding the most profitable customers, and how much does it cost to do so?
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[…]
Calculating the Lifetime Value of a Customer – This is a bit of a marketing nerd’s read but I still think it’s a good one to try to follow because it makes an important point about the value of long term customers
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Our wig client realizes about a 12x return on each email drop. However, they segment their lists according to a number of criteria and mail appropriate offers with relevant content and offers to each segment. Another of our clients sends out a newsletter 3x and only offers products for sale on the 4th email. This company’s conversion rate is exponentially higher because it has built a relationship and has trigger the “reciprocity rule” by giving something of value for free 3x’s.

[…] conversation on the issue of what is the right time horizon for computing LTV (life time value):https://www.kaushik.net/avinash/2…LTV is forward looking not backward looking. Of course, you can and should look backwards to get an […]

[…]
Long-Term Customer Value – Okay, so if traditional email stats dictate that your campaign was poor, look at the long-term customer value such as upsell opportunities, retention, referrals and overall “quality” of the sale. Calculating the Customer Lifetime Value (LTV) down to the campaign level, you will be able to discover the real value of your email campaigns, regardless of open rates or click-throughs.
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Some argue that happy customer referrals should also be included in this calculation as that reduces the cost of acquiring an additional client through direct marketing. Kaushik dismisses traditional marketing tools for internet marketing such as click through rates, visits, bounce rates and conversion rates as normal stuff with a focus on short term success and stresses on recognizing the value of CLTV. So essentially, it is a long term business model, not a short term success formula. He further argues that most often the emphasis is on value a customer adds as an average to gross profit over years. Which is where he thinks the mistake is, as this average hides certain high value and best customers who at a certain acquisition cost, order high value products, more number of times and for several years. So, these best customers add up to the revenue and profits far more than the average one’s do. But is it for every business to segregate its best and average customers?
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Step 4: Forecast Keyword Revenue Potential
For this step, you need to plug in a "profitability" computation, whether it be average value per sale, or lifetime value of a customer, or price per lead if you're doing lead gen, etc.
Note that you can also leave this column blank in your projection if you don't have access to this data yet, but then you're really only forecasting for potential traffic and conversions and not overall profitability.
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If Google shopping and Google flights are any indicator of the future, it's likely Google will put you on a diet of some kind of Adwords-type service you must adopt in order to keep you in the SERPs. That means you must be getting ready to master online advertising in your niche, which doesn't work without knowing your lifetime customer value, costs per customer acquisition and conversion rates. Who’s to say you can’t thrive under Google?
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However, there is another parameter that may be a more useful measurement of success: the Customer life time value (LTV). According to Avinash Kaushik (3), assessing customer LTV is preferable to simply generating sales metrics because customer LTV often leads to sales profits over the course of several years, while click-through sales last only until checkout. LTV customers bring real value to a business via product up and cross-sells, periodic purchases, higher purchase amounts, and referrals. By calculating the customer LTV of your email campaign, you are better able to learn of its long-term value.
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[…]
Long-Term Customer Value – Okay, so if traditional email stats dictate that your campaign was poor, look at the long-term customer value such as upsell opportunities, retention, referrals and overall “quality” of the sale. Calculating the Customer Lifetime Value (LTV) down to the campaign level, you will be able to discover the real value of your email campaigns, regardless of open rates or click-throughs.
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[…]
Many marketers believe that email marketing is dead. Ever since the CAN-SPAM Act and the difficulty of in-boxing emails, marketers tend to shy away from this tactic.The thing is… they are wrong! And I will take advantage of their unwillingness to leverage this tactic . Where they are wrong is when it comes to building your own email lists and marketing to them on a continual basis. This kind of email marketing is huge when it comes to bringing repeat customers to your site and generating more revenue from them. This is also known as increasing your customer life time value (LTV).
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Marketers have long looked at Metrics such as Cost/Click and Cost/Acquisition in allocating credit to their various Acquisition Sources and Channels. Nothing wrong with this (rather old school) approach except that when optimizing using these metrics, there will often be times when you end up investing in acquiring low value clientele. A not so recent article went into great detail articulating the thought process around why smart marketing should be about taking a long term view of the contribution of your various traffic sources and not the easy but rather myopic conversion metrics that are currently used so ubiquitously
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It’s very apparent in both SEO and Web design, business people seek more search traffic and greet most visitors with the same popping up message to subscribe even if they already do. As SEO gets harder and more expensive and people get increasingly annoyed with layers hiding content the focus will change to making existing clients or repeat visitors happy. Ultimately the point is to increase the lifetime customer value. A happy customer will buy again and again.
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[…]
For this step, you need to plug in a "profitability" computation, whether it be average value per sale, or lifetime value of a customer, or price per lead if you're doing lead gen, etc. Note that you can also leave this column blank in your projection if you don't have access to this data yet, but then you're really only forecasting for potential traffic and conversions and not overall profitability.
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If you’re optimizing for lifetime value, instead of one-time traffic, (something that would make Avinash Kaushik’s heart swell with joy), an approach that combines both link building and understanding search visibility with content that appeals to your target market, gets influencer attention, and moves prospects further down the funnel.
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Think about the lifetime value of your PPC click. Do your users buy once and then bail forever, or do they come back again for the same product or something else you offer? Factor in the lifetime value of your customer, and then build that into your PPC campaign budget. Then, think about how to nurture that post-click relationship to ensure you’re maximizing value, whether it’s email offers or something else.
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Academics have been working on this kind of problem since the advent of the commercial internet – and they have had success in creating one off models for predicting everything from the final price of a product auctioned on EBay to the lifetime value of a customer based on actions and clickstream.
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[…]
Your calculation doesn’t have to be that complex or far-reaching and you can also find great guidance by Avinash Kaushik in his post on computing lifetime value (including an Excel file to help you make the calculations). You could even go one step further and try to quantify and incorporate the referral value of happy customers that tell their friends about you.
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Comparing a long-term customer to a short-term customer will help justify whether to build out customer acquisition strategies or procure more resources. Segment your customers by traditional downloadable, order + upsell/cross sell, subscription, and SaaS. This analysis can be applied to different acquisition channels (i.e. customer arrived to your website through an ad, organic search, backlink etc.) to get an idea of how valuable each of these dimensions are. Here’s an example similar to Avanish Kaushik’s blog (a leading industry expert):
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The thing is… they are wrong! And I will take advantage of their unwillingness to leverage this tactic :) . Where they are wrong is when it comes to building your own email lists and marketing to them on a continual basis. This kind of email marketing is huge when it comes to bringing repeat customers to your site and generating more revenue from them. This is also known as increasing your customer life time value (LTV).
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Essentially this article is about creating loyal customers who will contribute to your company, instead of hoping for many one time purchases. You want the people that keep coming back for more, year after year and they want you to continue to offer the service they initially fell in love with. Calculate Customer Lifetime Value
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In a world with greater and greater choice finding the best customers, keeping them happy and understanding their value is vital for you to succeed long term. For a guide on getting started with measuring CLV this post from Avinash is a great place to get started.
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In a great article by Avinash Kaushik, the minute details of effective LTV calculations are inspected with the help of his friend David Hughes. Check out the article for more great insight on the mechanics of LTV. Included within the article is a handy Detailed Lifetime Value Model that you can work with yourself.
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More complex CLV models also take into account statistics such as: inflation, time period and variable bounce rates. Avinash Kaushik and KISSmetrics offer more extended explanations if you’re interested. But in short, CLV can be calculated by multiplying the profit per order with the average number of orders per customer.
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Huh..that scary algebra. I would love to demonstrate how you can calculate CLV of your brand but I don’t want to invent the wheel. There are plenty of good articles out there to make it easy for you to calculate your brand’s CLV. Excellent Analytics Tip #17: Calculate Customer Lifetime Value.
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It’s a complex subject that I’m not going to get into here in detail, but if you want to explore this more fully, I highly recommend you read Avinash Kaushik’s Guide to Calculating Customer Lifetime Value. I love his blog, and his big brain, and once you read this guide, I predict you’ll be likewise enamored. More importantly, thinking about CLV as he breaks it down will help you as you create your own customer retention strategies.
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There is a significant difference in the lifetime value of your Average customers and your Above Average Customers. [This article will enlighten you. Make certain that you are sitting down, as I know you will be quite surprised.] By relying on The Dreaded Five, as I like to call them, you literally are reaching into your corporate coffers and handing wads of cash to your competitors.
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[…]
It’s a complex subject that I’m not going to get into here in detail but if you want to explore this more fully, I highly recommend you read Avinash Kaushik’s Guide to Calculating Customer Lifetime Value. I love his blog, and his big brain, and once you read this guide, I predict you’ll be likewise enamored. More importantly, thinking about CLV as he breaks it down will help you as you create your own customer retention strategies.
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[…]
The above equation is your average order value multiplied by your purchase frequency (see above) multiplied by your average customer lifespan. If you don't know your average lifespan, you can use three years as a rough estimate.
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… and how should you actually do all this? Every business will have nuances but the foundation is a persistent connection between your data warehouse and digital analytics platform. Confidence in your data goes a long way too! With this link in place you can explore the digital predictors of customer value, build expected value models, export data to data management platforms, activate using different marketing channels, and more.
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You need to know a handful of things to calculate your customer lifetime value: Average monthly transactions, average order value, average customer lifespan and average gross margin. Without these inputs, you are on the path to sub-optimal, inefficient advertising. Or to put it another way, “faith-based” advertising that relies on guesses and good luck. And who has the time or money for that? To learn more about customer lifetime value, check out these excellent resources: Avinash Kaushik and David Hughes on calculating CLV
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